huggingface-jobs

You can run batch inference on Hugging Face cloud infrastructure by submitting arbitrary Python workloads through the hf_jobs MCP tool. This skill provisions managed CPUs, GPUs, or TPUs and automatically persists the inference results to a Hugging Face dataset repository.

Yes, you can execute batch inference tasks using a GPU-accelerated vLLM container on managed Hugging Face compute. The skill supports executing Docker containers to process large datasets efficiently without requiring local hardware setup.

You can schedule recurring ML workloads and automate data pipelines by triggering jobs via webhooks or setting up scheduled compute tasks. The skill manages job submission, hardware selection, and secure secret management for Hub authentication.

Yes, Hugging Face Jobs supports secure secret management for Hub authentication. This allows you to securely execute Python workloads and Docker containers on managed cloud infrastructure while protecting your access credentials.

The best way to generate synthetic data on Hugging Face cloud infrastructure is to submit a Python workload using this skill. It handles compute provisioning and automatically persists the generated outputs directly to a Hugging Face dataset repository.

No, you do not need local GPU hardware to run large-scale model training. This skill removes local infrastructure friction by allowing you to execute Python workloads directly on managed Hugging Face cloud infrastructure with hardware selection.